EmorZz1G/SimAD
SimAD, deep learning, anomaly detection, outlier detection, time series, TNNLS, 2025. "SimAD: A Simple Dissimilarity-based Approach for Time Series Anomaly Detection", time series anomaly detection
This project helps identify unusual behavior or critical incidents in long sequences of data that change over time, like sensor readings or system logs. It takes raw time series data as input and outputs clear indications of when anomalies occur. This is useful for engineers monitoring industrial equipment, cybersecurity analysts tracking network intrusions, or operations teams detecting system failures.
Use this if you need to reliably spot unexpected patterns or outliers in streaming or historical time series data to prevent issues or understand unusual events.
Not ideal if your data is static (not time-based) or if you are looking for simple threshold-based alerts rather than sophisticated pattern detection.
Stars
32
Forks
3
Language
Python
License
Apache-2.0
Category
Last pushed
Jan 19, 2026
Commits (30d)
0
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